IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i15p3034-d255246.html
   My bibliography  Save this article

A Monocular Vision-Based Framework for Power Cable Cross-Section Measurement

Author

Listed:
  • Xiaoming Zhang

    (School of Geodesy and Geomatics, Wuhan University, No.129, Luoyu Road, Wuhan 430079, China
    Collaborative Innovation Center for Geospatial Technology, No.129, Luoyu Road, Wuhan 430079, China)

  • Hui Yin

    (School of Geodesy and Geomatics, Wuhan University, No.129, Luoyu Road, Wuhan 430079, China
    Collaborative Innovation Center for Geospatial Technology, No.129, Luoyu Road, Wuhan 430079, China)

Abstract

The measurements of the diameter of different layers, the thickness of different layers and the eccentricity of insulation layer in the cross-section of power cables are important items of power cable test, which currently depend on labor-intensive manual operations. To improve efficiency, automatic measurement methods are in urgent need. In this paper, a monocular vision-based framework for automatic measurement of the diameter, thickness, and eccentricity of interest in the cross-section of power cables is proposed. The proposed framework mainly consists of three steps. In the first step, the images of cable cross-section are captured and undistorted with the camera calibration parameters. In the second step, the contours of each layer are detected in the cable cross-section image. In order to detect the complete and accurate contours of each layer, the structural edges in the cross-section image are firstly detected and divided into individual layers, then unconnected edges are connected by arc-based method, and finally contours are refined by the proposed break detection and grouping (BDG) and linear trend-based correction (LTBC) algorithm. In the third step, the monocular vision-based cross-section dimension measurement is accomplished by placing a chessboard coplanar with the power cable cross-section plane. The homography matrix mapping pixel coordinates to chessboard world coordinates is estimated, and the diameter, thickness and eccentricity of specific layers are calculated by homography matrix-based measurement method. Simulated data and actual cable data are both used to validate the proposed method. The experimental results show that diameter, minimum thickness, mean thickness and insulation eccentricity of simulated image without additive noise are measured with root mean squared error (RMSE) of 0.424, 0.103 and 0.063 mm, and 0.002, respectively, those of simulated image with additive Gaussian noise and salt and pepper noise are measured with RMSE of 0.502, 0.243 and 0.058 mm and 0.001. Diameter, minimum thickness and mean thickness of actual cable images are measured with average RMSE of 0.768, 0.308 and 0.327 mm. The measurement error of insulation eccentricity of actual cable image is comparatively large, and the measurement accuracy should be improved.

Suggested Citation

  • Xiaoming Zhang & Hui Yin, 2019. "A Monocular Vision-Based Framework for Power Cable Cross-Section Measurement," Energies, MDPI, vol. 12(15), pages 1-26, August.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:15:p:3034-:d:255246
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/15/3034/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/12/15/3034/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yanpeng Hao & Jie Wei & Xiaolan Jiang & Lin Yang & Licheng Li & Junke Wang & Hao Li & Ruihai Li, 2018. "Icing Condition Assessment of In-Service Glass Insulators Based on Graphical Shed Spacing and Graphical Shed Overhang," Energies, MDPI, vol. 11(2), pages 1-12, February.
    2. Haiyan Cheng & Yongjie Zhai & Rui Chen & Di Wang & Ze Dong & Yutao Wang, 2019. "Self-Shattering Defect Detection of Glass Insulators Based on Spatial Features," Energies, MDPI, vol. 12(3), pages 1-14, February.
    3. Sae Byul Kang & Bong Suk Sim & Jong Jin Kim, 2017. "Volume and Mass Measurement of a Burning Wood Pellet by Image Processing," Energies, MDPI, vol. 10(5), pages 1-13, May.
    4. Mehrtash Azizian Fard & Mohamed Emad Farrag & Scott McMeekin & Alistair Reid, 2018. "Electrical Treeing in Cable Insulation under Different HVDC Operational Conditions," Energies, MDPI, vol. 11(9), pages 1-14, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zahid Ali Siddiqui & Unsang Park, 2020. "A Drone Based Transmission Line Components Inspection System with Deep Learning Technique," Energies, MDPI, vol. 13(13), pages 1-24, June.
    2. Mehrtash Azizian Fard & Mohamed Emad Farrag & Alistair Reid & Faris Al-Naemi, 2019. "Electrical Treeing in Power Cable Insulation under Harmonics Superimposed on Unfiltered HVDC Voltages," Energies, MDPI, vol. 12(16), pages 1-15, August.
    3. Chuanyang Liu & Yiquan Wu & Jingjing Liu & Jiaming Han, 2021. "MTI-YOLO: A Light-Weight and Real-Time Deep Neural Network for Insulator Detection in Complex Aerial Images," Energies, MDPI, vol. 14(5), pages 1-19, March.
    4. Xiangxin Li & Ming Zhou & Yazhou Luo & Gang Wang & Lin Jia, 2018. "Effect of Ice Shedding on Discharge Characteristics of an Ice-Covered Insulator String during AC Flashover," Energies, MDPI, vol. 11(9), pages 1-11, September.
    5. Yong Liu & Qiran Li & Masoud Farzaneh & B. X. Du, 2020. "Image Characteristic Extraction of Ice-Covered Outdoor Insulator for Monitoring Icing Degree," Energies, MDPI, vol. 13(20), pages 1-12, October.
    6. Yanpeng Hao & Zhaohong Yao & Junke Wang & Hao Li & Ruihai Li & Lin Yang & Wei Liang, 2019. "A Classification Method for Transmission Line Icing Process Curve Based on Hierarchical K-Means Clustering," Energies, MDPI, vol. 12(24), pages 1-14, December.
    7. Jingjing Liu & Chuanyang Liu & Yiquan Wu & Huajie Xu & Zuo Sun, 2021. "An Improved Method Based on Deep Learning for Insulator Fault Detection in Diverse Aerial Images," Energies, MDPI, vol. 14(14), pages 1-19, July.
    8. Pierdicca, Roberto & Balestra, Mattia & Micheletti, Giulia & Felicetti, Andrea & Toscano, Giuseppe, 2022. "Semi-automatic detection and segmentation of wooden pellet size exploiting a deep learning approach," Renewable Energy, Elsevier, vol. 197(C), pages 406-416.
    9. Mejdi Jeguirim & Lionel Limousy, 2017. "Biomass Chars: Elaboration, Characterization and Applications," Energies, MDPI, vol. 10(12), pages 1-7, December.
    10. Antonino Imburgia & Pietro Romano & George Chen & Giuseppe Rizzo & Eleonora Riva Sanseverino & Fabio Viola & Guido Ala, 2019. "The Industrial Applicability of PEA Space Charge Measurements, for Performance Optimization of HVDC Power Cables," Energies, MDPI, vol. 12(21), pages 1-13, November.
    11. Stolarski, Mariusz J. & Stachowicz, Paweł & Dudziec, Paweł, 2022. "Wood pellet quality depending on dendromass species," Renewable Energy, Elsevier, vol. 199(C), pages 498-508.
    12. Zhaobin Wang & Yongke Lv & Runliang Wu & Yaonan Zhang, 2023. "Review of GrabCut in Image Processing," Mathematics, MDPI, vol. 11(8), pages 1-41, April.
    13. Paweł Mikrut & Paweł Zydroń, 2023. "Numerical Modeling of PD Pulses Formation in a Gaseous Void Located in XLPE Insulation of a Loaded HVDC Cable," Energies, MDPI, vol. 16(17), pages 1-21, September.
    14. Kamal Baharin, Nur Syahirah & Koesoemadinata, Vidya Cundasari & Nakamura, Shunsuke & Yahya, Wira Jazair & Muhammad Yuzir, Muhamad Ali & Md Akhir, Fazrena Nadia & Iwamoto, Koji & Othman, Nor’azizi & Id, 2020. "Conversion and characterization of Bio-Coke from abundant biomass waste in Malaysia," Renewable Energy, Elsevier, vol. 162(C), pages 1017-1025.
    15. Yixiang Zhang & Zongxi Zhang & Yuguang Zhou & Renjie Dong, 2018. "The Influences of Various Testing Conditions on the Evaluation of Household Biomass Pellet Fuel Combustion," Energies, MDPI, vol. 11(5), pages 1-11, May.
    16. Ju Wang & Guoqiang Wang & Xiaoxuan Hu & He Luo & Haiqing Xu, 2020. "Cooperative Transmission Tower Inspection with a Vehicle and a UAV in Urban Areas," Energies, MDPI, vol. 13(2), pages 1-17, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:15:p:3034-:d:255246. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.